Pre-Sale AI Makeover: $95M Field Services Business

Intro

Buyers don't discount you because they hate your business. They discount you because they don't trust your plumbing. Dispatch chaos, slow invoicing, and inconsistent documentation scream "future surprises."

This anonymized case study shows what BuildClub would implement ahead of a strategic sale—and the results you should expect when the work is executed fast and tied to KPI proof.

Profile (anonymized)

~$95M annual revenue
Multi-location industrial equipment / facilities services provider
11 branches across 5 states
EBITDA margin ~9%
Strategic sale in sight within 9 months

The situation

This company would have sticky contracts and long-term customer relationships, but operations would be old-school.

Dispatch & scheduling

Whiteboards, spreadsheets, and phone calls
Techs would zigzag across town while nearby jobs were planned "later"
First-time fix would be stuck around ~63%

Billing

Invoices would go out ~10–20 days after job completion
Change orders would be missed or under-billed

Customer experience

No unified asset history
Key accounts would complain about inconsistent updates and surprise invoices Potential buyers would like the revenue base but would already be talking about a discount.

BuildClub Solution

1) AI dispatch & routing

Consolidate data:
Add an AI layer that would: Expected results (after ~6 months)
Tech utilization: 62% → 72% (billable vs paid hours)
First-time fix: 63% → 76%
Truck rolls per job down ~12% Expected financial impact
Efficiency equivalent to ~14 tech FTEs at ~$95k fully loaded
Growth absorbed with the same team (instead of cutting headcount)
EBITDA benefit: ~+$1.0M/year
Fuel and maintenance savings: ~+$280k/year
Total dispatch EBITDA impact: ~+$1.28M/year

2) Quote-to-cash automation with LLMs

Deploy an LLM "job-to-invoice" assistant that would:
Back office would review and post invoices quickly, with fewer disputes and fewer missed billables Expected results (after ~5 months)
Time from job completion to invoice: 11.5 days → 2.7 days
Properly billed change orders up ~18%
Write-offs tied to bad documentation down ~$350k/year Expected financial impact
Working-capital benefit from faster billing ≈ $1.4M
Extra recognized revenue ≈ $1.1M/year
At ~30% incremental margin → ~$330k EBITDA
Reduced write-offs: +$350k EBITDA
Total quote-to-cash EBITDA impact: ~$680k/year

3) Customer & asset intelligence layer

Combine service history, asset-level issues, and SLA performance per account
Make it queryable in natural language, e.g.: Expected results
Clearer proof of recurring revenue quality
Evidence of upsell/cross-sell opportunities
Lower perceived churn risk in key accounts
A "data-rich platform" story instead of a "nice tuck-in" story

Expected pre-transaction outcome (run-rate, ~9 months)

Revenue: ~$95M → ~$101M
EBITDA: ~$8.6M (9.0%) → ~$11.0M (~10.8%)

What typically drives the EBITDA delta

Dispatch: +$1.28M
Quote-to-cash: +$0.68M
Overhead & small wins: +$0.4M

Valuation impact (illustrative)

Before: ~8.5x on ~$8.6M → ~$73M enterprise value
After: ~10x on ~$11.0M → ~$110M enterprise value Even with conservative attribution, this would typically protect/create ~$20–30M of value by fixing operational plumbing before a buyer prices the risk.

If you're heading into a process

If you're going to market soon, fix dispatch, billing, and documentation first. It's the difference between "premium platform" and "please holdback 20% in escrow."

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We work with operators, PE-backed businesses, and professional services firms to ship outcomes — not decks.